{"id":"https://openalex.org/W7143545673","doi":"https://doi.org/10.55592/cilamce2025.v5i.14434","title":"Development of Neural Model of Gas Processing Plant","display_name":"Development of Neural Model of Gas Processing Plant","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7143545673","doi":"https://doi.org/10.55592/cilamce2025.v5i.14434"},"language":"en","primary_location":{"id":"doi:10.55592/cilamce2025.v5i.14434","doi":"https://doi.org/10.55592/cilamce2025.v5i.14434","is_oa":false,"landing_page_url":"https://doi.org/10.55592/cilamce2025.v5i.14434","pdf_url":null,"source":{"id":"https://openalex.org/S4210168516","display_name":"Anais do ... Congresso Ibero-Latino-Americano de M\u00e9todos Computacionais em Engenharia","issn_l":"2178-4949","issn":["2178-4949","2675-6269"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE)","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159781257","display_name":"Bruno Sousa","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bruno Sousa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022444942","display_name":"Leonardo Azevedo Sc\u00e1rdua","orcid":"https://orcid.org/0000-0002-5390-0024","observed_orcids":["https://orcid.org/0000-0002-5390-0024"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leonardo Azevedo Scardua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9053000211715698,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9053000211715698,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.03590000048279762,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.03539999946951866,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gas-processing-plant","display_name":"gas processing plant","score":0.9760000109672546},{"id":"https://openalex.org/keywords/neural-network-model","display_name":"neural network model","score":0.6420000195503235},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.7599999904632568},{"id":"https://openalex.org/keywords/dynamic-behavior-prediction","display_name":"dynamic behavior prediction","score":0.800000011920929},{"id":"https://openalex.org/keywords/digital-twin","display_name":"digital twin","score":0.9419999718666077},{"id":"https://openalex.org/keywords/multi-step-forecasting","display_name":"multi-step forecasting","score":0.9480000138282776}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.777899980545044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46459999680519104},{"id":"https://openalex.org/C74078090","wikidata":"https://www.wikidata.org/wiki/Q2287558","display_name":"Natural-gas processing","level":3,"score":0.4634000062942505},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38029998540878296},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.296099990606308},{"id":"https://openalex.org/C3018541784","wikidata":"https://www.wikidata.org/wiki/Q5526318","display_name":"Gas analysis","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C90322556","wikidata":"https://www.wikidata.org/wiki/Q1981169","display_name":"Neural gas","level":4,"score":0.289900004863739},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C2992367939","wikidata":"https://www.wikidata.org/wiki/Q4121144","display_name":"Gas consumption","level":2,"score":0.2635999917984009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.55592/cilamce2025.v5i.14434","doi":"https://doi.org/10.55592/cilamce2025.v5i.14434","is_oa":false,"landing_page_url":"https://doi.org/10.55592/cilamce2025.v5i.14434","pdf_url":null,"source":{"id":"https://openalex.org/S4210168516","display_name":"Anais do ... Congresso Ibero-Latino-Americano de M\u00e9todos Computacionais em Engenharia","issn_l":"2178-4949","issn":["2178-4949","2675-6269"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE)","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"},{"score":0.5690000057220459,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2540529999","https://openalex.org/W4210974915","https://openalex.org/W7130955323","https://openalex.org/W2083240652","https://openalex.org/W1907528338","https://openalex.org/W4221073079","https://openalex.org/W2969122600","https://openalex.org/W2361640734","https://openalex.org/W3033234821","https://openalex.org/W1528641349"],"abstract_inverted_index":{"The":[0,121,179,189],"total":[1],"volume":[2],"of":[3,29,42,49,74,83,96,108,117,123,143,170,176,181,202],"gas":[4,32,39,50,62,98,160],"processed":[5,38],"in":[6,31,36,57],"Brazil":[7],"is":[8],"forecast":[9],"to":[10,17,46,91,101,140,198],"increase":[11],"over":[12],"the":[13,18,27,37,47,61,72,75,78,81,106,110,141,168,174,182,200],"next":[14],"decade,":[15],"according":[16],"ten-year":[19],"energy":[20],"expansion":[21],"plan":[22],"(EPE).":[23],"As":[24],"a":[25,58,93,97,127,159],"result,":[26],"occurrence":[28],"failures":[30],"processing":[33,51,99,161],"could":[34],"result":[35],"being":[40],"out":[41],"specification":[43],"and/or":[44],"lead":[45],"interruption":[48],"operations":[52,118],"(availability":[53],"and":[54,77,130],"reliability),":[55],"especially":[56],"scenario":[59],"where":[60],"comes":[63],"from":[64,155,158],"different":[65,69],"extraction":[66],"points,":[67],"with":[68,105,147,167],"characteristics.":[70],"Given":[71],"complexity":[73],"plant":[76,162],"existing":[79],"non-linearities,":[80],"use":[82,142],"Deep":[84],"Neural":[85],"Networks":[86],"(Deep":[87],"Learning)":[88],"was":[89],"sought":[90],"build":[92],"neural":[94,112,124,171,184],"model":[95,113],"plant,":[100],"predict":[102],"dynamic":[103,177],"behavior,":[104],"prospect":[107],"using":[109],"evaluated":[111],"for":[114,135,173],"continuous":[115,136],"monitoring":[116],"(Digital":[119],"Twin).":[120],"choice":[122],"networks":[125],"follows":[126],"more":[128,133],"current":[129],"fast-response":[131],"approach,":[132],"suitable":[134],"monitoring,":[137],"when":[138],"compared":[139],"rigorous":[144],"phenomenological":[145],"models,":[146],"long":[148],"response":[149],"times.":[150],"A":[151],"real":[152],"dataset,":[153],"obtained":[154],"data":[156],"collected":[157],"(UPGN),":[163],"will":[164,186,191],"be":[165,187,192],"used,":[166],"generation":[169],"models":[172,185,190],"prediction":[175,196],"behavior.":[178],"performance":[180],"various":[183],"compared.":[188],"tested":[193],"across":[194],"multiple":[195],"horizons":[197],"determine":[199],"effectiveness":[201],"multi-step-ahead":[203],"predictions.":[204]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-03-31T00:00:00"}